Hospital

With the recent emphasis on public health preparedness, health departments are identifying new ways to prepare for emergencies. There has been a significant increase in the number of syndromic surveillance systems operating in recent years. These systems are based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of public health emergencies. Like other states, Rhode Island sought to enhance its traditional surveillance activities through the implementation of such a system. Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed by the University of Pittsburgh’s Center for Biomedical Informatics. Data from three hospitals were collected as part of the pilot implementation of the Rhode Island RODS system. Personnel at both hospitals and the Department of Health, trained in surveillance-related areas such as infection control and epidemiology, received access to RI RODS. As part of the evaluation framework, Rhode Island desired to assess system user attitudes and opinions towards the new system.

Objective

This paper presents results of a survey assessing syndromic surveillance system initial user satisfaction and attitudes regarding syndromic surveillance.

Syndromic surveillance had been implemented in Dongcheng District with a view to probing into the feasibility of establishing a syndromic surveillance system in major Chinese cities, sieving syndromic surveillance indicators applicable to the eruption of infectious respiratory tract and digestive tract diseases, and attempting the operating method of data collection in different locations such as hospital and drug stores in Dongcheng of Beijing China.

Objective

The project has fund donated by World Bank under joint management of WHO and Ministry of Health of P.R.China , The target was try to build up a syndromic surveillance system in Beijing.

Syndromic surveillance has traditionally been used by public health in disease epidemiology. Partnerships between hospital-based and public health systems can improve efforts to monitor for disease clusters. Greenville Hospital System operates a syndromic surveillance system, which uses EARS-X to monitor chief complaint, lab, and radiological data for the four emergency departments within the hospital system. Combined, the emergency departments have approximately 145,000 visits per year. During March 2007 an increase in invasive group A Streptococcus (GAS) disease in the community lead to the use of syndromic surveillance to determine if there was a concomitant increase in Scarlet Fever within the community.

Objective

 Demonstrate the utility of collaboration between hospital-based and public health syndromic surveillance systems in disease investigation. Demonstrate the ability of syndromic surveillance in identification and evaluation of process improvements.

This paper outlines the integration of hospital admission, Febrile Respiratory Illness (FRI) screening and Canadian Triage and Acuity Score (CTAS) data streams within an Emergency Department Syndromic Surveillance system. These data elements allow better characterization of outbreak severity and enable more effective resource allocation within acute care settings.

This paper examines the continued usefulness, through the 2005-06 influenza season, of a hospital admissions-based syndromic surveillance system as a supplement to laboratory and clinical influenza surveillance in preparation for pandemic influenza.

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

BioSense is a national program designed to improve the nation’s capabilities for conducting disease detection, monitoring, and real-time situational awareness. Currently, BioSense receives near real-time data from non-federal hospitals, as well as national daily batched data from the Departments of Defense and Veteran’s Affairs facilities.  These data are analyzed, visualized, and made simultaneously available to public health at local, state, and federal levels through the BioSense application.

Objective:

In this paper we present summary information on the non-federal hospitals currently sending data to the BioSense system and describe this distribution by hospital type, method of data delivery as well as patient class and patient health indicator.

Timely and accurate syndromic surveillance depends on continuous data feeds from healthcare facilities. Typical outlier detection methodologies in syndromic surveillance compare predictions of counts for an interval to observed event counts, either to detect increases in volume associated with public health incidents or decreases in volume associated with compromised data transmission. Accurate predictions of total facility volume need to account for significant variance associated with the time of day and week; at the extreme are facilities which are only open during limited hours and on select days. Models need to account for the cross-product of all hours and days, creating a significant data burden. Timely detection of outages may require sub-hour aggregation, increasing this burden by increasing the number of intervals for which parameters need to be estimated. Nonparametric models for the probability of message arrival offer an alternative approach to generating predictions. The data requirements are reduced by assuming some time-dependent structure in the data rather than allowing each interval to be independent of all others, allowing for predictions at sub-hour intervals.

Objective:

Characterize the behavior of nonparametric regression models for message arrival probability as outage detection tools.

In 2005, the Cook County Department of Public Health (CCDPH) began using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) as an emergency department (ED)-based local syndromic surveillance program (LSSP); 23 (100%) of 23 hospitals in suburban Cook County report to the LSSP. Data are transmitted in delimited ASCII text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. Discharge diagnosis and disposition are optional data elements. Prior to 2017, the Illinois Department of Public Health placed facilities participating in the Cook LSSP in a holding queue to transform their flat file submissions into a HL7 compliant message; however as of 2017, eligible hospitals must submit HL7 formatted production data to IDPH to fulfill Meaningful Use. The primary syndromic surveillance system for Illinois is the National Syndromic Surveillance Program (NSSP), which transitioned to an ESSENCE interface in 2016. As of December 2016, 20 (87%) of 23 hospitals reporting to the LSSP also reported to IDPH and the NSSP. As both syndromic surveillance systems aim to collect the same data, and now can be analyzed with the same interface, CCDPH sought to compare the LSSP and NSSP for data completeness, consistency, and other attributes.

Objective:

This analysis was undertaken to determine how the data completeness, consistency, and other attributes of our local syndromic surveillance program compared to the National Syndromic Surveillance Platform.